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Applying GIS in Analysing Black Spot Areas in Penang, Malaysia

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Applying GIS in Analysing Black Spot Areas in Penang, Malaysia ISSN 2354-9114 (online), ISSN 0024-9521 (print) Indonesian Journal of Geography Vol. 50, No.2, December 2018 (133 - 144) DOI: http://dx.doi.org/10.22146/ijg.27440, website: https://jurnal.ugm.ac.id/ijg ©2018 Faculty of Geography UGM and The Indonesian Geographers Association Applying GIS in Analysing Black Spot Areas in Penang, Malaysia Tarmiji Masron, Wan Muhammad Taufik Wan Hussin, Mohd Norarshad Nordin, Nur Faziera Yaakub and Mohd Azizul Hafiz Jamian Received: 2017-08-13 /Revision: 2018-07-13/ Accepted : 2018-08-01 © 2018. Faculty of Geography UGM and The Indonesian Geographers Association Abstract This study aims to analyze fatal accident rate involving all vehicle types in the North East District of Penang. It covers fatal accident data within the duration of three years from 2011 till 2013. The primary objective is to analyze the spatial pattern and fatal accident black spot areas using Geographic Information System (GIS) application. Average Nearest Neighbor (ANN) tool is used to analyze fatal accident spatial pattern, while Kernel Density Estimation (KDE) method is utilized for fatal accident analysis. The Fatal Accident rates in 2011, 2012 and 2013 were the highest with each accounted up to 90, 88 and 91 cases. The result of ANN shows that the fatal accident pattern for 2011, 2012 and 2013 is clustered with null hypothesis rejected. The KDE analysis result shows that most fatal accident black spot areas happened at main road areas or segments. Keywords: Fatal accident, GIS, spatial pattern, black spot, Kernel Abstrak Penelitian ini bertujuan untuk menganalisis tingkat kecelakaan fatal yang melibatkan semua jenis kendaraan di Kabupaten Timur Laut Penang. Ini mencakupi data kecelakaan fatal dalam tiga tahun dari 2011 hingga 2013. Tujuan utamanya adalah untuk menganalisis pola ruangan dan area bintik hitam kecelakaan menggunakan aplikasi Sistem Infor- masi Geografis (SIG). Average Nearest Neighboor (ANN) digunakan untuk menganalisis pola spasial dari kecelakaan fatal, sementara Kernal Density Estimation (KDE) metode digunakan untuk analisis kecelakaan fatal. Tingkat kematian pada tahun 2011, 2012 dan 2013 adalah yang tertinggi dengan masing-masing berkontribusi hingga 90, 88 dan 91 kasus. Hasil ANN menunjukkan bahwa pola kecelakaan fatal untuk 2011, 2012 dan 2013 dikelompokkan dengan menolak “hipotesis nol”. Hasil analisis KDE menunjukkan bahwa lokasi utama kecelakaan fatal terjadi di jalan atau segmen utama. Kata kunci: Kecelakaan fatal, GIS, pola spasial, titik hitam, Kernel 1.Introduction and 2012 (OECD, 2014). Analyzing road accidents is Generally, urbanisation involves the shifta complex process as the researches are from various in population from rural to urban settlements backgrounds, including engineering, geography, as well (McGranahan & Satterthwaite, 2014). In Malaysia, as human behaviors (Sabel et al., 2005). urbanization refers to the gazetted area and the criteria However, the main influencing factors can used in 1970, 1980, 1991 and 2000 Population Census be classified into three categories, which are road (Masron et al., 2012). The area gazetted as urban engineering and traffic conditions, vehicle features areas must have a population of 10,000 and more and and capacities, as well as drivers’ behaviors and gazetted areas with their adjoining built-up areas and performance. Various improvements have been made, the combination of both areas have a total population and steps are taken to reduce the number of road of 10,000 or more when the Population Census 2000 accidents, particularly in the aspects of better road was conducted (Department of Statistics, 1995 & 2001). safety and safer vehicle design, but the most significant According to the Global Road Safety Report, it is stated elements in overcoming this problem are the behavior that there are more than 1.2 million victims of accidents and performance of the drivers (Clinton & John, 2013). every year, and nearly 50 million people are injured on The study finds that traffic engineering and road roads (WHO, 2009). Based on the statement by Masron network feature operations are the background and et al. (2012), Department of Statistics, 1995 & 2001 context that need to be taken into consideration in the and WHO (2009), it gives an idea that the higher the aspects of road safety (Brockenbrough, 2009). Elvik et number of people, the higher the number of accident al. (2009) also find that in a road engineering global cases. Meanwhile, Road Safety Annual Report 2014 study, the best safety priorities include separating shows that the success in reducing road accidents at traffic on both directions according to the types of the global level is still low, although there is an increase vehicles, having better junction design, creating safety in world mobility which is 0.6 percent between 2011 monitoring and improving black spot areas, as well as © 2018 by the authors. Licensee Indonesian Journal of Geography, Indonesia. traffic signage along the roads. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY NC) license. https://creativecommons.org/licenses/by-nc/4.0/. Moreover, in examining the Road Accident Theory, Tarmiji Masron, Wan Muhammad Taufik Wan Hussin, Mohd Norarshad Nordin, Nur it is also significant to also look into Human Behavior Faziera Yaakub and Mohd Azizul Hafiz Jamian Centre for Spatially Integrated Digital Humanities, Faculty of Social Sciences and Humanities, General Theory which shows that drivers do not adhere Universiti Malaysia Sarawak (UNIMAS),94300 Kota Samarahan Sarawak Malaysia to road safety rules while driving. Many researchers Correspondent email: [email protected] GIS IN ANALYSING FATAL ACCIDENT Tarmiji Masron, et al. agree that the insouciant attitude of drivers as the main data on accidents must be able to be mapped based on factor that causes road accidents. Among the models a certain platform. that could be referred in identifying human behaviors In addition, Yang et al. (2013) highlight an approach is through Interface Theory, which illustrates a general in identifying an area that has the potential of accident model of human driving behaviors (Fuller, 2000; occurrence, as well as loss total caused by the accidents. Fuller & Santos, 2002). According to the model, the The research result shows that the accident rate and task difficulty level can be identified from a dynamic economic cost due to traffic accidents are high in St. interface which is between the demand of the driving Louis Country and St. Louis City and most accidents task and the ability of the driver. If the ability surpasses that occurred was at junction areas and changes in traffic the demand, hence the driving task becomes easy; direction. Rankavat & Tiwari (2013), on the other hand, whereas, if the ability is at par with the demand, the calculate accident density within 50 meter radius with driver is considered to be at the maximum level of his the size of 1 square kilometer. The research result shows driving capability, which is at a difficult driving level. that the accident density that involved pedestrians is The situation becomes worse if the demand surpasses high in Delhi area, which has high population density. the capability as a person with this level of driving capability could cause driving failure where he could 2.The Methods lose control of the vehicle, and the unfortunate situation This study focuses on the North East District of where a collision could occur or the vehicle could skid Penang. Based on Figure 1 below, the study area is part off the road. of the island with the width of 121 square km, and it is Currently, the use of GIS has increased among connected to Seberang Perai through the Penang Bridge. agencies that manage the road network system for the The location of the area is at 5° 22’ 16.28’’ N latitude purpose of analyzing road accident data. Identifying and 100° 14’ 14.22’’ E longitude. The North East District problematic road locations is the most important is the most developed and populated area in Penang. aspect in an accident case study. Although there is no The local authority for this district is City Council of international standard definition in identifying areas Penang Island. The district is divided into 7 mukim with frequent road accident occurrences, in the study (counties) which are Mukim 13, Mukim 14, Mukim 15, conducted by Mark et al. (2013), the term “black spot Mukim 16, Mukim 17, Mukim 18 and George Town area” is used to differentiate road area that has higher City. Other than that, the district had a total of 508 181 accident total with other areas. In another research population with a density of 4200 people per square by Oulha et al. (2013), two approaches are utilized kilometer in 2010. The total for overall fatal accidents in identifying black spot areas where road accidents within the district in 2011, 2012 and 2013 were 90, 88 occur which are through analyzing object and human and 91 cases each. The total fatal accidents in North movement analysis approach, as well as Kernel Density East District were the second highest in Penang after Estimation analysis approach. Based on the analysis, the Central Seberang Perai District with 101, 115 and black spot areas are identified by taking into account 101 cases for each year in 2011, 2012, and 2013. This areas having repeated accidents of three to five cases a district also has 13 police border areas and 83 police year. sector areas. Among the police stations included in the In previous studies, there are different definitions study area are Lebuh Pantai, Dato Keramat, Central, of “black spot areas” among researchers. This could Patani Road, Kampung Baru, Batu Feringghi, Tanjung be due to various factors, such as the number of cases Tokong, Ayer Itam, Bandar Baru, Komuniti Taman or accident locations, the width of the study area, etc. Desa Permai, Jelutong, Sungai Nibong, and Pulau Tikus Nordin and Masron (2016) state that the use of GIS in Police Stations.
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